OBJECTIVE: This study aims to assess the diagnostic efficacy of Korean Thyroid imaging reporting and data system (K-TIRADS), S-Detect software and contrast-enhanced ultrasound (CEUS) when employed individually, as well as their combined application, for the evaluation of thyroid nodules, with the objective of identifying the optimal method for diagnosing thyroid nodules.
METHODS: Two hundred and sixty eight cases pathologically proven of thyroid nodules were retrospectively enrolled. Each nodule was classified according to K-TIRADS. S-Detect software was utilized for intelligent analysis. CEUS was employed to acquire contrast-enhanced features.
RESULTS: The area under curve (AUC) values for diagnosing benign and malignant thyroid nodules using K-TIRADS alone, S-Detect software alone, CEUS alone, the combined application of K-TIRADS and CEUS, the combined application of S-Detect software and CEUS were 0.668, 0.668, 0.719, 0.741, and 0.759, respectively (p < 0.001). The sensitivity rate of S-Detect software was 89.9% (p < 0.001). It was the highest of the five diagnostic methods above.
CONCLUSIONS: The utilization of S-Detect software can be served as a powerful tool for early screening. Notably, the combined utilization of S-Detect software with CEUS demonstrates superior diagnostic performance compared to employing K-TIRADS, S-Detect software, CEUS used individually, as well as the combined application of K-TIRADS with CEUS.

UNASSIGNED: The detection rate of benign thyroid nodules is increasing every year, with some affected patients experiencing symptoms. Ultrasound-guided thermal ablation can reduce the volume of nodules to alleviate symptoms. As the degree and speed of lesion absorption vary greatly between individuals, an effective model to predict curative effect after ablation is lacking. This study aims to predict the efficacy of ultrasound-guided thermal ablation for benign thyroid nodules using machine learning and explain the characteristics affecting the nodule volume reduction ratio (VRR).
UNASSIGNED: Prospective study.
UNASSIGNED: The clinical and ultrasonic characteristics of patients who underwent ultrasound-guided thermal ablation of benign thyroid nodules at our hospital between January 2020 and January 2023 were recorded.
UNASSIGNED: Six machine learning models (logistic regression, support vector machine, decision tree, random forest, eXtreme Gradient Boosting [XGBoost], and Light Gradient Boosting Machine [LGBM]) were constructed to predict efficacy; the effectiveness of each model was evaluated, and the optimal model selected. SHapley Additive exPlanations (SHAP) was used to visualize the decision process of the optimal model and analyze the characteristics affecting the VRR.
UNASSIGNED: In total, 518 benign thyroid nodules were included: 356 in the satisfactory group (VRR ≥70% 1 year after operation) and 162 in the unsatisfactory group. The optimal XGBoost model predicted satisfactory efficacy with 78.9% accuracy, 88.8% precision, 79.8% recall rate, an F1 value of 0.84 F1, and an area under the curve of 0.86. The top five characteristics that affected VRRs were the proportion of solid components < 20%, initial nodule volume, blood flow score, peripheral blood flow pattern, and proportion of solid components 50-80%.
UNASSIGNED: The models, based on interpretable machine learning, predicted the VRR after thermal ablation for benign thyroid nodules, which provided a reference for preoperative treatment decisions.

BACKGROUND: A fine-needle aspiration (FNA) biopsy is a basic procedure to verify the morphology of thyroid nodules. However, significant variations in the diagnostic performance of FNA, as well as fluctuations in the rate of malignancy (ROM) in different categories of the Bethesda System for Reporting Thyroid Cytopathology, have been discovered in many studies. The development and employment of additional molecular tests in daily clinical practice require an understanding of the prevalence and structure of thyroid malignancy in each category of the Bethesda system.
METHODS: Current research is a continuous retrospective cohort study of the results of the examination and treatment of 1652 patients with thyroid tumors who have undergone primary surgery in 2021 at a national referral thyroid cancer center. FNA diagnostic performance was studied by comparing cytological diagnoses with histological outcomes. The authors evaluated ROM in each Bethesda category and identified the tumors representing a diagnostic pitfall.
RESULTS: The overall ROM was 72.4%. When considering ROM in each Bethesda category, it was 16.7% in Bethesda I, 10.1% in Bethesda II, 23.0% in Bethesda IV, 74.8% in Bethesda V, 99.4% in Bethesda VI. The false-positive rate was 65.0%, false negative-0.67%. Follicular and oncocytic adenomas, as well as the follicular variant of papillary thyroid cancer, were the most troublesome entities for patients\' management.
CONCLUSIONS: ROM was comparable to the values implied by the Bethesda system. Follicular tumors were the main source of high false-positive rates. This necessitates the refinement of existing tests and the development of new diagnostic tests to overcome the abovementioned problems.

Composed of over 1200 species of anaerobes and aerobes bacteria along with bacteriophages, viruses, and fungal species, the human gut microbiota (GM) is vital to health, including digestive equilibrium, immunologic, hormonal, and metabolic homeostasis. Micronutrients, usually refer to trace elements (copper, iodine, iron, selenium, zinc) and vitamins (A, C, D, E), interact with the GM to influence host immune metabolism. So far, microbiome studies have revealed an association between disturbances in the microbiota and various pathological disorders, such as anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis, anxiety, depression, early-onset cancers, type 1 diabetes (T1D) and type 2 diabetes (T2D). As common conditions, thyroid diseases, encompassing Graves\' disease (GD), Graves\' orbitopathy (GO), Hashimoto\'s thyroiditis (HT), benign nodules, and papillary thyroid cancer (TC), have negative impacts on the health of all populations. Following recent studies, GM might play an integral role in triggering diseases of the thyroid gland. Not only do environmental triggers and genetic predisposing background lead to auto-aggressive damage, involving cellular and humoral networks of the immune system, but the intestinal microbiota interacts with distant organs by signals that may be part of the bacteria themselves or their metabolites. The review aims to describe the current knowledge about the GM in the metabolism of thyroid hormones and the pathogenesis of thyroid diseases and its involvement in the appearance of benign nodules and papillary TC. We further focused on the reciprocal interaction between GM composition and the most used treatment drugs for thyroid disorders. However, the exact etiology has not yet been known. To elucidate more precisely the mechanism for GM involvement in the development of thyroid diseases, future work is needed.

Due to the frequent diagnosis of benign thyroid nodules, it is necessary to deviate from the traditional paradigm based on frequent surgical treatment. This article highlights the evolution of diagnosis and treatment in recent years, beginning from standardization of ultrasound assessment of nodules and cytology results to minimally invasive techniques to reduce the size of symptomatic thyroid nodules. These achievements reduce the number of surgeries, enable more individualized care for patients with benign thyroid disease, reduce long-term complications, and promote cost-effectiveness within healthcare systems. Furthermore, although the use of minimally invasive techniques significantly decreases thyroid nodule volume, the thyroid nodule usually does not disappear and the challenges in this field are discussed (the efficacy of thermal ablation, a variable part of thyroid nodules that remains viable after thermal ablation, some of the nodules treated with thermal ablation may require a second treatment over time and the efficacy of thermal ablation in nodules with different phenotypes). However, although surgery still represents the \"gold standard\" for establishing the final histopathologic diagnosis, it is associated with lifelong thyroid hormone substitution need and serious complications in rare cases. Therefore, it should represent the ultima ratio only after a detailed diagnostic procedure. In the future, artificial intelligence-assisted programs for the evaluation and management of nodules are expected.

UNASSIGNED: A preoperative diagnosis to distinguish malignant from benign thyroid nodules accurately and sensitively is urgently important. However, existing clinical methods cannot solve this problem satisfactorily. The aim of this study is to establish a simple, economic approach for preoperative diagnosis in eastern population.
UNASSIGNED: Our retrospective study included 86 patients with papillary thyroid cancer and 29 benign cases. The ITK-SNAP software was used to draw the outline of the area of interest (ROI), and Ultrosomics was used to extract radiomic features. Whole-transcriptome sequencing and bioinformatic analysis were used to identify candidate genes for thyroid nodule diagnosis. RT-qPCR was used to evaluate the expression levels of candidate genes. SVM diagnostic model was established based on the METLAB 2022 platform and LibSVM 3.2 language package.
UNASSIGNED: The radiomic model was first established. The accuracy is 73.0%, the sensitivity is 86.1%, the specificity is 17.6%, the PPV is 81.6%, and the NPV is 23.1%. Then, CLDN10, HMGA2, and LAMB3 were finally screened for model building. All three genes showed significant differential expressions between papillary thyroid cancer and normal tissue both in our cohort and TCGA cohort. The molecular model was established based on these genetic data and partial clinical information. The accuracy is 85.9%, the sensitivity is 86.1%, the specificity is 84.6%, the PPV is 96.9%, and the NPV is 52.4%. Considering that the above two models are not very effective, We integrated and optimized the two models to construct the final diagnostic model (C-thyroid model). In the training set, the accuracy is 96.7%, the sensitivity is 100%, the specificity is 93.8%, the PPV is 93.3%, and the NPV is 100%. In the validation set, the accuracy is 97.6%, the sensitivity remains 100%, the specificity is 84.6%, the PPV is 97.3%, and the NPV is 100%.
UNASSIGNED: A diagnostic panel is successfully established for eastern population through a simple, economic approach using only four genes and clinical data.

Ultrasound (US) is the primary tool for evaluating patients with thyroid nodules, and the risk of malignancy assessed is based on US features. These features help determine which patients require fine-needle aspiration (FNA) biopsy. Classification systems for US features have been developed to facilitate efficient interpretation, reporting, and communication of thyroid US findings. These systems have been validated by numerous studies and are reviewed in this article. Additionally, this overview provides a comprehensive description of the clinical and laboratory evaluation of patients with thyroid nodules, various imaging modalities, grayscale US features, color Doppler US, contrast-enhanced US (CEUS), US elastography, FNA biopsy assessment, and the recent introduction of molecular testing. The potential of artificial intelligence in thyroid US is also discussed.

Thyroid nodules in pediatric patients are less common than in adults but show a higher malignancy rate. Accordingly, the management of thyroid nodules in pediatric patients is more complex the younger the patient is, needing careful evaluation by physicians. In adult patients, specific ultrasound (US) features have been associated with an increased risk of malignancy (ROM) in thyroid nodules. Moreover, several US risk stratification systems (RSSs) combining the US features of the nodule were built to define the ROM. RSSs are developed for the adult population and their use has not been fully validated in pediatric patients. This study aimed to evaluate the available data about US features of thyroid nodules in pediatric patients and to provide a summary of the evidence regarding the performance of RSS in predicting malignancy. Moreover, insights into the management of thyroid nodules in pediatric patients will be provided.

UNASSIGNED: Emerging evidence suggests alterations in gut microbiota (GM) composition following thyroid nodules (TNs) development, yet the causal relationship remains unclear. Utilizing Mendelian Randomization (MR), this study aims to elucidate the causal dynamics between GM and TNs.
UNASSIGNED: Employing summary statistics from the MiBioGen consortium (n=18,340) and FinnGen consortium (1,634 TNs cases, 263,704 controls), we conducted univariable and multivariable MR analyses to explore the GM-TNs association. Techniques including inverse variance weighted, MR-Egger regression, weighted median, and MR-PRESSO were utilized for causal inference. Instrumental variable heterogeneity was assessed through Cochran\'s Q statistic and leave-one-out analysis. Reverse MR was applied for taxa showing significant forward MR associations, with multivariate adjustments for confounders.
UNASSIGNED: Our findings suggest that certain microbiota, identified as Ruminococcaceae_NK4A214_group (OR, 1.89; 95%CI, 0.47-7.64; p = 0.040), Senegalimassilia (OR, 1.72; 95%CI, 1.03-2.87; p =0.037), Lachnospiraceae (OR,0.64; 95%CI,0.41-0.99; p =0.045), exhibit a protective influence against TNs\' development, indicated by negative causal associations. In contrast, microbiota categorized as Desulfovibrionales (OR, 0.63; 95%CI, 0.41-0.95; p =0.028), Prevotella_7 (OR, 0.79; 95%CI, 0.63-1.00; p =0.049), Faecalibacterium (OR, 0.66; 95%CI, 0.44-1.00; p =0.050), Desulfovibrionaceae (OR, 0.55; 95%CI, 0.35-0.86; p =0.008), Deltaproteobacteria (OR, 0.65; 95%CI, 0.43-0.97; p =0.036) are have a positive correlation with with TNs, suggesting they may serve as risk factors. Reverse MR analyses did not establish significant causal links. After comprehensive adjustment for confounders, taxa Desulfovibrionales (Order), Desulfovibrionaceae (Family), Deltaproteobacteria (Class) remain implicated as potential contributors to TNs\' risk.
UNASSIGNED: This study substantiates a significant causal link between GM composition and TNs development, underscoring the thyroid-gut axis\'s relevance. The findings advocate for the integration of GM profiles in TNs\' prevention and management, offering a foundation for future research in this domain.

UNASSIGNED: The incidence rate of thyroid nodules has reached 65%, but only 5-15% of these modules are malignant. Therefore, accurately determining the benign and malignant nature of thyroid nodules can prevent unnecessary treatment. We aimed to develop a deep-learning (DL) radiomics model based on ultrasound (US), explore its diagnostic efficacy for benign and malignant thyroid nodules, and verify whether it improved the diagnostic level of physicians.
UNASSIGNED: We retrospectively included 1,076 thyroid nodules from 817 patients at three institutions. The radiomics and DL features of the US images were extracted and used to construct radiomics signature (Rad_sig) and deep-learning signature (DL_sig). A Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used for feature selection. Clinical US semantic signature (C_US_sig) was constructed based on clinical information and US semantic features. Next, a combined model was constructed based on the above three signatures in the form of a nomogram. The model was constructed using a development set (institution 1: 719 nodules), and the model was evaluated using two external validation sets (institution 2: 74 nodules, and institution 3: 283 nodules). The performance of the model was assessed using decision curve analysis (DCA) and calibration curves. Furthermore, the C_US_sigs of junior physicians, senior physicians, and expers were constructed. The DL radiomics model was used to assist the physicians with different levels of experience in the interpretation of thyroid nodules.
UNASSIGNED: In the development and validation sets, the combined model showed the highest performance, with areas under the curve (AUCs) of 0.947, 0.917, and 0.929, respectively. The DCA results showed that the comprehensive nomogram had the best clinical utility. The calibration curves indicated good calibration for all models. The AUCs for distinguishing between benign and malignant thyroid nodules by junior physicians, senior physicians, and experts were 0.714-0.752, 0.740-0.824, and 0.891-0.908, respectively; however, with the assistance of DL radiomics, the AUCs reached 0.858-0.923, 0.888-0.944, and 0.912-0.919, respectively.
UNASSIGNED: The nomogram based on DL radiomics had high diagnostic efficacy for thyroid nodules, and DL radiomics could assist physicians with different levels of experience to improve their diagnostic level.